还真有-T5模型就是既有encoder又有decoder,其在判别式任务上的效果与Bert相当,但是在生成式任务上效果实际可能并没有同尺寸decoder-only模型好。 下面针对这三种类型:encoder-only,encoder-decoder和decoder-only的模型框架我们进行分析,看看它们之间到底有什么关系,每种结构又适合做什么任务。 从上图我们可以明显看出对...
The goal of the blog post is to give anin-detailexplanation ofhowthe transformer-based encoder-decoder architecture modelssequence-to-sequenceproblems. We will focus on the mathematical model defined by the architecture and how the model can be used in inference. Along the way, we will give so...
Encoder-Decoder和 Seq2SeqEncoder-Decoder是NLP 领域里的一种模型框架。它被广泛用于机器翻译、语音识别等任务。 本文将详细介绍...,而是一类算法的统称。Encoder-Decoder算是一个通用的框架,在这个框架下可以使用不同的算法来解决不同的任务。Encoder-Decoder这个框架很好的诠释了机器学习的核心思路: 将 ...
一种直接的办法就是加上decoder做预测生成,这就形成了encoder-decoder架构,如下所示 Classic Transformer Block decoder第一个MHA变成masked-MHA,使用的是前文casual的attention mask的方式,这样每个当前输出token只能看到过去生成的token decoder新增第二个MHA,并且K和V来自于encoder的输出,这样就实现了看到原始输入的全文...
Seq2Seq模型有时可以包含自编码和自回归模型。Seq2Seq模型的decoder通常是自回归的,它根据之前的所有token,一次生成一个token的输出序列。 Seq2Seq的encoder部分可以看作类似自编码器,因为它将输入压缩成密集表示,但是与自编码器LM不同的是,seq2seq的encoder目标不是重建输入,而是为生成的输出序列(通常是不同domain...
技术标签:NLP seq2seq model: encoder-decoder 1.1. its probablistic model 1.2. RNN encoder-decoder model architecture context vector c = encoder’s final state i.e. fixed global representation of the input sequ... 查看原文 encoder-decoder框架和普通框架的区别在哪里?
Neural Machine Translation using LSTMs and Attention mechanism. Two approaches were implemented, models, one without out attention using repeat vector, and the other using encoder decoder architecture and attention mechanism. nlpnatural-language-processingpytorchlstmnltkrnnseq2seqneural-machine-translationatte...
Thetransformer-basedencoder-decoder model was introduced by Vaswani et al. in the famousAttention is all you need paperand is today thede-factostandard encoder-decoder architecture in natural language processing (NLP). Recently, there has been a lot of research on differentpre-trainingobjectiv...
Encoder-decoder models are a type of neural network architecture that is used in a variety of natural language processing (NLP) tasks, such as machine translation, text summarization, and question-answering. They are also known as sequence-to-sequence models. ...
The introduction of transformer architecture changed the NLP paradigm and distinguished itself from recurrent models by enabling the processing of sentences as a whole rather than word byword. The attention mechanisms introduced in Transformers allowed them to understand the relationship between far-apart ...